Cluster formation as a measure of interpretability in multiple testing.
Multiple test procedures are usually compared on various aspects of error control and power. Power is measured as some function of the number of false hypotheses correctly identified as false. However, given equal numbers of rejected false hypotheses, the pattern of rejections, i.e. the particular set of false hypotheses identified, may be crucial in interpreting the results for potential application.In an important area of application, comparisons among a set of treatments based on random samples from populations, two different approaches, cluster analysis and model selection, deal implicitly with such patterns, while traditional multiple testing procedures generally focus on the outcomes of subset and pairwise equality hypothesis tests, without considering the overall pattern of results in comparing methods. An important feature involving the pattern of rejections is their relevance for dividing the treatments into distinct subsets based on some parameter of interest, for example their means. This paper introduces some new measures relating to the potential of methods for achieving such divisions. Following Hartley (1955), sets of treatments with equal parameter values will be called clusters. Because it is necessary to distinguish between clusters in the populations and clustering in sample outcomes, the population clusters will be referred to as P -clusters; any related concepts defined in terms of the sample outcome will be referred to with the prefix outcome. Outcomes of multiple comparison procedures will be studied in terms of their probabilities of leading to separation of treatments into outcome clusters, with various measures relating to the number of such outcome clusters and the proportion of true vs. false outcome clusters. The definitions of true and false outcome clusters and related concepts, and the approach taken here, is in the tradition of hypothesis testing with attention to overall error control and power, but with added consideration of cluster separation potential.The pattern approach will be illustrated by comparing two methods with apparent FDR control but with different ways of ordering outcomes for potential significance: The original Benjamini-Hochberg (1995) procedure (BH), and the Newman-Keuls (Newman, 1939; Keuls, 1952) procedure (NK).
Shaffer
Juliet Popper
JP
University of California, Department of Statistics, 367 Evans Hall 3860, Berkeley, CA 94720-3860, USA. shaffer@stat.berkeley.edu
D001699:Biometry / Q000379:methods*; D016000:Cluster Analysis; D006801:Humans; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care